{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "电力窃漏电用户的自动识别的笔记：\n",
    "目标：\n",
    "1、归纳漏电用户的关键特征，构建漏电用户的model\n",
    "2、利用事实监控的数据，懂所有的用户进行实时诊断\n",
    "注意的点：\n",
    "1、某一些大用户不可能存在漏电行为，例如银行、学校和工商等。\n",
    "2、漏电用户的窃电开始时间和结束时间是表征其漏电的关键节点，在这些节点上，用户的用电负荷和终端报警数据会有一定的变化。\n",
    "   样本数据抽取是务必包含一定范围的数据，并通过用户的负荷数据计算出当天的用户的用电量。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "数据探索分析：\n",
    "1、分布分析：  所有漏电用户的分布分析，显示每个类别的漏电用户的分布。\n",
    "2、周期性分析：随机抽取正常用电用户额窃电用户，采用周期性分析对用电量进行探索。 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAGDCAYAAADUGkKJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCSEFcBERkRD6f21ZXfld3W0+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f228f20c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAGDCAYAAADUGkKJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFZAJXJKkAvp/TYGH7bjtjn8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f228f20cdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*- conding: utf-8 -*-\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "# 正常显示中文和正负号\n",
    "plt.rcParams[\"font.serif\"] = [\"SimHei\"]\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "normalUser = pd.read_csv(\"data/normalUser.csv\")\n",
    "unnormalUser = pd.read_csv(\"data/unnormalUser.csv\")\n",
    "\n",
    "normalUser.plot(title=u\"正常用电用户的电量趋势\",x=normalUser[\"日期\"],figsize=(8,6))\n",
    "unnormalUser.plot(title=u\"漏电用电用户的电量趋势\",x=unnormalUser['日期'],figsize=(8,6))                \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "数据预处理\n",
    "1、数据清洗：目的是从业务和建模的相关方面考虑，筛选出需要的数据。\n",
    "    1、通过数据探索发现非居民用电类别中不存在漏电的现象，故将非居民类别过滤掉。\n",
    "    2、结合本案例，节假日用电量与工作相比，明显偏低，为了尽可能达到较好的效果，过滤掉节假日的用电数据\n",
    "2、缺失值处理\n",
    "    1、缺失值少的情况下，一般情况下不会直接去掉数据，而是对缺失值进行填充，这里选择拉格朗日插值法进行补充。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from scipy.interpolate import lagrange #导入lagrange函数\n",
    "\n",
    "inputfile = \"data/missing_data.xls\"\n",
    "outputfile = \"data/missing_data_processed.xls\"\n",
    "\n",
    "data = pd.read_excel(inputfile)\n",
    "\n",
    "# 自定义列向量差值函数\n",
    "# s为列向量，n是被插值的位置，k是前后的取的个数 默认是5\n",
    "\n",
    "def polyinterp_column(s, n, k=5):\n",
    "    y = s[list(range(n-k,n)) + list(range(n+1,n+1+k))]\n",
    "    y = y[y.notnull()]\n",
    "\n",
    "    return lagrange(y.index, list(y))(n)\n",
    "\n",
    "for i in data.columns:\n",
    "    for j in range(len(data)):\n",
    "        if data[i].isnull()[j]:\n",
    "            data[i][j] = polyinterp_column(data[i],j)\n",
    "            \n",
    "            \n",
    "data.to_excel(outputfile,header=None, index=False)            \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "数据变换：\n",
    "1、电量趋势下降指标： 用电量的趋势\n",
    "    考虑第i天前后5天的用电量的斜率， k[i] = sum[i-5,i+5]{(f-mean(f))(l-mean(l))} / sum[i-5,i+5]{(l-mean(j))^2}    \n",
    "    mean(f) = 1/11 sum[i-5, i+5](f) ; mean(l) = 1/11 sum[i-5, i+5](l)\n",
    "    \n",
    "    如果用电趋势，不断下滑，则认为有窃电嫌疑，计算这11天内的，当天比前一天用电量趋势递减的天数\n",
    "    if(k[i] < k[i-1]) D[i] = 1 \n",
    "    else k[i] >= k[i-1] D[i] = 0\n",
    "     \n",
    "    T = sum[i-5, i+5](D[j])\n",
    "2、线损指标： 线损增长率\n",
    "    线损率用来衡量供电线路的损失比例，线损率是总电量s与实际用电量sum(f)的差除以总电量s。\n",
    "    以天为单位进行计算。\n",
    "    t = s / sum(f)\n",
    "    如果用户发生窃电，则当天的线损率会上升，用户每天用电量存在波动，以天为单文，误差较大，\n",
    "    所以考虑前后几天（5天）的线损率的平均值，判断增长率是否大于1%，若大于1%，则认为是窃电。\n",
    "    前5天的线损率平均值V[i1],后5天的平均值v[i2],若v[i1]比v[i2]的增长率大于1%，认为有窃电嫌疑。\n",
    "    if((v[i1] - v[i2]) / v[i2] > 1%) E[i] = 1\n",
    "    else E[i] = 0\n",
    "    \n",
    "3、告警类指标： 与窃电相关的终端告警数\n",
    "    计算发生与漏电相关的报警的总次数。\n",
    "\n",
    "得到训练的数据。\n",
    "1、进行模型的构建：\n",
    "    1、数据划分，0.8作为训练集，0.2作为测试集\n",
    "    2、LM神经网络模型：\n",
    "        输入节点：3\n",
    "        输出节点：1\n",
    "        隐藏节点：10\n",
    "        优化函数：Adam\n",
    "        激活函数：Relu(x)\n",
    "    3、CART决策树模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 分割数据作为训练数据和测试数据\n",
    "\n",
    "from random import shuffle # 用来打乱数据\n",
    "\n",
    "datafile = \"data/model.xls\"\n",
    "\n",
    "data = pd.read_excel(datafile)\n",
    "data = data.as_matrix()\n",
    "\n",
    "shuffle(data) #打乱数据\n",
    "\n",
    "p = 0.8 #训练数据的比例\n",
    "train = data[0:int(len(data)*p)]\n",
    "test = data[int(len(data)*p):]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "`Dense` can accept only 1 positional arguments ('units',), but you passed the following positional arguments: [3, 10]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-f8d0e2101ab7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#初始化\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     41\u001b[0m                                     \u001b[0;34m', but you passed the following '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m                                     \u001b[0;34m'positional arguments: '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m                                     str(list(args[1:])))\n\u001b[0m\u001b[1;32m     44\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalue_conversions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: `Dense` can accept only 1 positional arguments ('units',), but you passed the following positional arguments: [3, 10]"
     ]
    }
   ],
   "source": [
    "# 构建LM神经网络模型\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "modelfile = 'tmp/net.model' #存储训练好的神经网络\n",
    "\n",
    "model = Sequential() #初始化\n",
    "\n",
    "model.add(Dense(3,10))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tmp/tree.pkl']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CART决策树模型\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "\n",
    "treefile = 'tmp/tree.pkl' #模型的输出的名字\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(train[:,:3],train[:,3]) \n",
    "\n",
    "#保存模型,写入文件\n",
    "from sklearn.externals import joblib\n",
    "joblib.dump(tree, treefile) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2、模型的评价\n",
    "    采用ROC曲线进行评估，一个优秀的分类器所对应的ROC曲线应该尽量靠近左上角。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 177 into shape (59,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-d783bd4623ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mroc_curve\u001b[0m \u001b[0;31m#导入ROC曲线\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpredict_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mfpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthresholds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredict_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 177 into shape (59,)"
     ]
    }
   ],
   "source": [
    "# LM神经网络模型的ROC曲线\n",
    "from sklearn.metrics import roc_curve #导入ROC曲线\n",
    "\n",
    "predict_result = model.predict(test[:,:3].reshape(len(test)))\n",
    "fpr, tpr, thresholds = roc_curve(test[:,3], predict_result, pos_label=1)\n",
    "\n",
    "plt.plot(fpr,tpr,linewidth=2, label = 'ROC of LS')\n",
    "plt.xlabel('False Position Rate')\n",
    "plt.ylabel('True Position Rate')\n",
    "\n",
    "#边界范围\n",
    "plt.xlim(0,1.05)\n",
    "plt.ylim(0,1.05)\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22785adc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 决策树模型的ROC曲线\n",
    "from sklearn.metrics import roc_curve #导入ROC曲线\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(test[:,3], tree.predict_proba(test[:,:3])[:,1], pos_label=1)\n",
    "\n",
    "plt.plot(fpr,tpr,linewidth=2, label = 'ROC of CART')\n",
    "plt.xlabel('False Position Rate')\n",
    "plt.ylabel('True Position Rate')\n",
    "\n",
    "#边界范围\n",
    "plt.xlim(0,1.05)\n",
    "plt.ylim(0,1.05)\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
